{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "## 随机森林"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "c70291278fc3239a"
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2025-05-22T13:49:39.441200Z",
     "start_time": "2025-05-22T13:49:38.860306Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pickle\n",
    "import os\n",
    "import time\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score, precision_recall_fscore_support, confusion_matrix, classification_report\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 加载数据及数据预处理"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "25465d8d3fced90e"
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "# 数据集路径\n",
    "data_dir = r'D:\\Machine_learning\\jiqixuexi\\cifar-10-python\\cifar-10-batches-py'\n",
    "\n",
    "# 加载CIFAR-10数据集\n",
    "def load_cifar10_data(data_dir):\n",
    "    # 加载训练数据\n",
    "    train_data = []\n",
    "    train_labels = []\n",
    "    for i in range(1, 6):\n",
    "        batch_path = os.path.join(data_dir, f'data_batch_{i}')\n",
    "        with open(batch_path, 'rb') as file:\n",
    "            batch = pickle.load(file, encoding='latin1')\n",
    "            train_data.append(batch['data'])\n",
    "            train_labels.extend(batch['labels'])\n",
    "    train_data = np.array(train_data).reshape(50000, 3, 32, 32).transpose(0, 2, 3, 1)\n",
    "    train_labels = np.array(train_labels)\n",
    "\n",
    "    # 加载测试数据\n",
    "    test_batch_path = os.path.join(data_dir, 'test_batch')\n",
    "    with open(test_batch_path, 'rb') as file:\n",
    "        test_batch = pickle.load(file, encoding='latin1')\n",
    "    test_data = test_batch['data'].reshape(10000, 3, 32, 32).transpose(0, 2, 3, 1)\n",
    "    test_labels = np.array(test_batch['labels'])\n",
    "\n",
    "    return (train_data, train_labels), (test_data, test_labels)\n",
    "\n",
    "# 调用函数加载数据\n",
    "(train_data, train_labels), (test_data, test_labels) = load_cifar10_data(data_dir)\n",
    "\n",
    "# 数据预处理\n",
    "train_data = train_data.astype('float32') / 255.0\n",
    "test_data = test_data.astype('float32') / 255.0\n",
    "\n",
    "# 将图像数据展平为一维向量\n",
    "train_data = train_data.reshape(-1, 32*32*3)\n",
    "test_data = test_data.reshape(-1, 32*32*3)\n",
    "\n",
    "# 使用PCA降维\n",
    "pca = PCA(n_components=0.95)  # 保留95%的方差\n",
    "train_data = pca.fit_transform(train_data)\n",
    "test_data = pca.transform(test_data)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-05-22T14:16:10.136093Z",
     "start_time": "2025-05-22T14:00:34.331038Z"
    }
   },
   "id": "3bfe8255066f0428",
   "execution_count": 2
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 创建模型及模型训练与评估"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "db1fd99f4e16a67d"
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练时间: 102.79秒\n",
      "Accuracy: 0.4402\n",
      "Precision: 0.4334468013183765\n",
      "Recall: 0.4402\n",
      "F1-score: 0.43353058938506184\n",
      "Confusion Matrix:\n",
      " [[536  39  45  26  32  19  30  34 186  53]\n",
      " [ 27 527  13  40  19  39  27  30  87 191]\n",
      " [108  36 253  76 177  76 135  53  52  34]\n",
      " [ 55  52  75 219  64 209 150  52  48  76]\n",
      " [ 54  19 116  53 416  54 155  66  40  27]\n",
      " [ 29  39  81 162  65 364  99  85  37  39]\n",
      " [ 15  28  71  65 110  52 568  28  27  36]\n",
      " [ 38  42  43  65  98  99  54 414  48  99]\n",
      " [102  86  11  23  16  40  19  22 598  83]\n",
      " [ 54 175  10  33  19  22  38  36 106 507]]\n",
      "Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.53      0.54      0.53      1000\n",
      "           1       0.51      0.53      0.52      1000\n",
      "           2       0.35      0.25      0.29      1000\n",
      "           3       0.29      0.22      0.25      1000\n",
      "           4       0.41      0.42      0.41      1000\n",
      "           5       0.37      0.36      0.37      1000\n",
      "           6       0.45      0.57      0.50      1000\n",
      "           7       0.50      0.41      0.45      1000\n",
      "           8       0.49      0.60      0.54      1000\n",
      "           9       0.44      0.51      0.47      1000\n",
      "\n",
      "    accuracy                           0.44     10000\n",
      "   macro avg       0.43      0.44      0.43     10000\n",
      "weighted avg       0.43      0.44      0.43     10000\n"
     ]
    }
   ],
   "source": [
    "# 创建随机森林模型\n",
    "clf = RandomForestClassifier(n_estimators=100, max_depth=20, random_state=42)\n",
    "\n",
    "# 模型训练\n",
    "start_time = time.time()\n",
    "clf.fit(train_data, train_labels)\n",
    "train_time = time.time() - start_time\n",
    "print(f\"训练时间: {train_time:.2f}秒\")\n",
    "\n",
    "# 模型预测\n",
    "y_pred = clf.predict(test_data)\n",
    "\n",
    "# 模型评估\n",
    "accuracy = accuracy_score(test_labels, y_pred)\n",
    "precision, recall, f1, _ = precision_recall_fscore_support(test_labels, y_pred, average='weighted')\n",
    "conf_matrix = confusion_matrix(test_labels, y_pred)\n",
    "class_report = classification_report(test_labels, y_pred)\n",
    "\n",
    "# 输出结果\n",
    "print(\"Accuracy:\", accuracy)\n",
    "print(\"Precision:\", precision)\n",
    "print(\"Recall:\", recall)\n",
    "print(\"F1-score:\", f1)\n",
    "print(\"Confusion Matrix:\\n\", conf_matrix)\n",
    "print(\"Classification Report:\\n\", class_report)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-05-22T14:25:41.515179Z",
     "start_time": "2025-05-22T14:23:58.468702Z"
    }
   },
   "id": "24ec3d16277c32c0",
   "execution_count": 3
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 使用热力图展示混淆矩阵"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "bce7608a0acc16e6"
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1200x800 with 2 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制混淆矩阵热力图\n",
    "plt.figure(figsize=(12, 8))\n",
    "sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues')\n",
    "plt.title(\"Random Forest - Confusion Matrix (CIFAR-10)\")\n",
    "plt.xlabel(\"Predicted Label\")\n",
    "plt.ylabel(\"True Label\")\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-05-22T14:27:14.849427Z",
     "start_time": "2025-05-22T14:27:14.141772Z"
    }
   },
   "id": "cd6505b373a4e8e5",
   "execution_count": 4
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   },
   "id": "55bdfb30a0c26eb9"
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
